usage.md

Usage

By default, we use DistributedDataParallel (DDP) both in single GPU and multiple GPU cases for simplicity.

Training

torchrun --nnodes={num_node} --nproc_per_node={num_gpu} --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py {config}

• num_node is often set as 1 if all gpus are allocated in a single node. num_gpu is the number of used GPU.
• config is the path of the config file.

Example:

• Training feature-based ActionFormer on 1 GPU.

torchrun \
    --nnodes=1 \
    --nproc_per_node=1 \
    --rdzv_backend=c10d \
    --rdzv_endpoint=localhost:0 \
    tools/train.py configs/actionformer/thumos_i3d.py

• Training end-to-end-based AdaTAD on 4 GPUs within 1 node.

torchrun \
    --nnodes=1 \
    --nproc_per_node=4 \
    --rdzv_backend=c10d \
    --rdzv_endpoint=localhost:0 \
    tools/train.py configs/adatad/e2e_anet_videomae_s_adapter_frame768_img160.py

Note:

• GPU number would affect the detection performance in most cases. Since TAD dataset is small, and the number of ground truth actions per video differs dramatically in different videos. Therefore, the recommended setting for training feature-based TAD is 1 GPU, empirically.
• By default, evaluation is also conducted in the training, based on the argument in the config file. You can disable this, or increase the evaluation interval to speed up the training.

Inference and Evaluation

torchrun --nnodes={num_node} --nproc_per_node={num_gpu} --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py {config} --checkpoint {path}

• if checkpoint is not specified, the best.pth in the config's result folder will be used.

Example:

• Inference and Evaluate ActionFormer on 1 GPU.

torchrun \
    --nnodes=1 \
    --nproc_per_node=1 \
    --rdzv_backend=c10d \
    --rdzv_endpoint=localhost:0 \
    tools/test.py \
    configs/actionformer/thumos_i3d.py \
    --checkpoint exps/thumos/actionformer_i3d/gpu1_id0/checkpoint/epoch_34.pth